Information inference for agronomic data generation in sugarcane applications

Abstract

A method for mapping sugarcane crop yield, the method comprising: receiving signals during a harvesting operation from a yield sensor, which senses a yield characteristic of a harvested sugarcane crop, and a processing sensor, which senses a processing characteristic of the sugarcane crop and is associated with the sugarcane harvester; determining a measured sugarcane crop yield using the received signals; determining a calibrated sugarcane crop yield using at least the measured sugarcane crop yield; and generating a sugarcane crop yield map with the calibrated sugarcane crop yield and a georeferenced location of the sugarcane crop yield associated with the location of the sugarcane harvester during the harvest.

Description

FIELD OF THE DISCLOSURE[0001]The present disclosure relates generally to sensor fusion system for a sugarcane harvester, wherein the sensor fusion system is used to detect and map one or more void crop plants and one or more crop yields.BACKGROUND OF THE DISCLOSURE[0002]In many applications, it can be important to know an operating state of an agricultural work machine. Current systems combine values from several sensors to determine the operating state of the machine that may vary over time to automatically control components of the work machine. However, for numerous reasons signals from one such sensor can be less reliable than those from another sensor, be it due to the type of sensor, operating state, conditions, failure or signal degradation. For example, some sensors, e.g., trash or leaf sensors, are less reliable in high throughput or high moisture conditions than in low throughput or dry conditions.SUMMARY OF THE DISCLOSURE[0003]A method for mapping an agricultural crop in ...

AI Technical Summary

Benefits of technology

The patent describes a method for mapping void crop plants in a field using signals from a yield sensor and a processing sensor on an agricultural machine. The method involves determining the presence of a void crop plant and its location in the field. The method can use fuzzy logic, machine learning, or statistical analysis to classify the signals from the sensors. The method can also include generating a void crop map based on the calibrated crop yield and its location in the field. The technical effect of the patent is to provide a more accurate way of identifying and mapping void crop plants in a field, which can help improve crop yield and management.

Problems solved by technology

However, for numerous reasons signals from one such sensor can be less reliable than those from another sensor, be it due to the type of sensor, operating state, conditions, failure or signal degradation.

For example, some sensors, e.g., trash or leaf sensors, are less reliable in high throughput or high moisture conditions than in low throughput or dry conditions.

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